Humic substance coagulation: Artificial neural network simulation

Desalination

Volumn 253, Issue 1-3, 2010, Pages 153-157

  Al Abri, Mohammed ^a   Al Anezi, Khalid ^b   Dakheel, Akram ^c   Hilal, Nidal ^d  

^a SULTAN QABOOS UNIVERSITY   (Oman)

^b The Public Authority for Applied Education and Training   (Kuwait)

^c AL BAATH UNIVERSITY   (Syrian Arab Republic)

^d UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

ANN; Humic acid; Polymer coagulation; Prediction

Indexed keywords

ANN; ARTIFICIAL NEURAL NETWORK SIMULATION; BACK-PROPAGATION NEURAL NETWORKS; DATA SETS; HUMIC ACID; HUMIC SUBSTANCES; OVERFITTING; PREDICTION ACCURACY; REGRESSION COEFFICIENT; TRAINING DATA; UV ABSORBANCE;

AGGLOMERATION; BIOLOGICAL MATERIALS; COAGULATION; COMPUTER SIMULATION; HEAVY METALS; HYDRAULIC STRUCTURES; NEURONS; ORGANIC ACIDS; POLYELECTROLYTES;

NEURAL NETWORKS;

ABSORBANCE; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; COAGULATION; COMPUTER SIMULATION; ELECTROLYTE; EXPERIMENTAL STUDY; HUMIC SUBSTANCE; PREDICTION;

EID: 76049092125     PISSN: 00119164     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.desal.2009.11.014     Document Type: Article

References (30)

1. Basheer I.A., Lakshmi N.R., and Najjar Y. Site characterization by neuronets: an application to the landfill siting problem. Ground Water 34 (1996) 610-617
2. Bowen R., Jones G., Welfoo J., and Yousef H.N. Predicting salt rejections at nanofiltration membranes using artificial neural networks. Desalination 129 (2000) 147-162
3. Mellit A., Benghanem M., and Kalogirou S.A. Modelling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: proposition for a new sizing procedure. Renewable Energy 32 (2007) 285-313
4. Zhang X.M., Mesirov J.P., and Waltz D.L. Hybrid system for protein secondary structure prediction. Journal of Molecular Biology 225 (1992) 1040-1063
5. Fang Q., Biby G., Haque E., Hannah M.A., and Spillman C.K. Neural network modeling of physical properties of ground wheat. Cereal Chemistry 75 (1998) 251-253
6. Rodriguez M.J., and Serodes J.B. Assessing empirical linear and non-linear modelling of residual chlorine in urban drinking water systems. Environmental Modelling & Software 14 (1999) 93-102
7. Cybenko G. Approximation by superpositions of a sigmoidal function. Math. Control Signals System 2 (1989) 303-314
8. Hornik K., Stinchombe M., and White H. Multilayer feedforward networks are universal approximators. Neural Networks 2 (1989) 359-366
9. Chen H., and Kim A.S. Prediction of permeate flux decline in crossflow membrane filtration of colloidal suspension: a radial basis function neural network approach. Desalination 192 (2006) 415-428
10. Curcio S., Calabro V., and Iorio G. Reduction and control of flux decline in cross-flow membrane processes modelled by artificial neural networks. Membrane Science 286 (2006) 125-132
11. Darwish N.A., Hilal N., Al-Zoubi H., and Mohammad A.W. Neural networks simulation of the filtration of sodium chloride and magnesium chloride solutions using nanofiltration membranes. Chemical Engineering Research & Design 85 (2007) 417-430
12. Sahoo G.B., and Ray C. Predicting flux decline in crossflow membranes using artificial neural networks and genetic algorithms. Membrane Science 283 (2006) 147-157
13. Zhao Y., Taylor J.S., and Chellam S. Predicting RO/NF water quality by modified solution diffusion model and artificial neural networks. Membrane Science 263 (2005) 38-46
14. Icer S., Kara S., and Guven A. Comparison of multilayer perceptron training algorithms for portal venous Doppler signals in the cirrhosis disease. Expert Systems with Applications 31 (2006) 406-413
15. Abdi H., Valentin D., Edelman B., O'Toole J., and WidroweHoff A.A. Learning rule for a generalization of the linear auto-associator. Mathematical Psychology 40 (1996) 175-182
16. El Tabach E., Lancelot L., Shahrour I., and Najjar Y. Use of artificial neural network simulation metamodelling to assess groundwater contamination in a road project. Mathematical and Computer Modelling 45 (2007) 766-776
17. Ghaffari A., Abdollahi H., Khoshayand M.R., Bozchalooi I.S., Dadgar A., and Rafiee-Tehrani M. Performance comparison of neural network training algorithms in modeling of bimodal drug delivery. International Journal of Pharmaceutics 327 (2006) 126-138
18. Guthrie J.W., Hassan N.M., Salam M.S.A., Fasfous I.I., Murimboh C.A., Murimboh J., Chakrabarti C.L., and Gregoire D.C. Complexation of Ni, Cu, Zn, and Cd by DOC in some metal-impacted freshwater lakes: a comparison of approaches using electrochemical determination of free-metal-ion and labile complexes and a computer speciation model, WHAM V and VI. Analytica Chimica Acta 528 (2005) 205-218
19. De Paolis F., and Kukkonen J. Binding of organic pollutants to humic and fulvic acids: influence of pH and the structure of humic material. Chemosphere 34 (1997) 1693-1704
20. Klavins M., and Eglite L. Immobilisation of humic substances. Colloids and Surfaces A: Physicochemical and Engineering Aspects 203 (2002) 47-54
21. Town R.M., and Filella M. Crucial role of the detection window in metal ion speciation analysis in aquatic systems: the interplay of thermodynamic and kinetic factors as exemplified by nickel and cobalt. Analytica Chimica Acta 466 (2002) 285-293
22. Sanudo-Wilhelmy S.A., Rivera-Duarte I., and Flegal A.R. Distribution of colloidal trace metals in the San Francisco Bay estuary. Geochimica et Cosmochimica Acta 60 (1996) 4933-4944
23. Dupre B., Viers J., Dandurand J., Polve M., Benezeth P., Vervier P., and Braun J. Major and trace elements associated with colloids in organic-rich river waters: ultrafiltration of natural and spiked solutions. Chemical Geology 160 (1999) 63-80
24. Edzwald J.K., Becker W.C., William C., and Wattier K.L. Surrogate parameters for monitoring organic matter and THM precursors. American Water Works Association 77 (1985) 122-132
25. Kam S., and Gregory J. The interaction of humic substances with cationic polyelectrolytes. Water Research 35 (2001) 3557-3566
26. Kuo C., and Amy G.L. Factors affecting coagulation with aluminium sulphate-II: dissolved organic matter removal. Water Research 22 (1988) 863-872
27. Hilal N., Al-Abri M., Moran A., and Al-Hinai H. Effects of heavy metals and polyelectrolytes in humic substance coagulation under saline conditions. Desalination 220 (2008) 85-95
28. Ghedira H., and Bernier M. The effect of some internal neural network parameters on SAR texture classification performance. IEEE geoscience and remote sensing symposium 6 (2004) 3845-3848
29. Garcia-Pedrajas N., Ortiz-Boyer D., and Hervas-Martinez C. An alternative approach for neural network evolution with a genetic algorithm: crossover by combinatorial optimization. Neural Networks 19 (2006) 514-528
30. Saxen H., and Pettersson F. Method for the selection of inputs and structure of feedforward neural networks. Computers & Chemical Engineering 30 (2006) 1038-1045